Energy management technology, including predictive analytics, data-as-a-service (DaaS), comparable to EMaaS and PaaS systems, and machine learning are creating new opportunities for management facilities professionals. This heightened level of energy management technology results in direct savings and improvements to the productivity, operability, and sustainability in modern enterprises.

These technologies are designed specifically to address the key concerns that existed within previous energy management systems, such as sacrificing comfort in favor of savings. Consequently, modern facility managers need to understand how these energy management technologies improve comfort, reduce impact on the environment and energy reliance, use rule-based analytics to drive values, and how machine learning analyzes both data and information against external and internal factors, without the stipulation of rule-defined algorithms, to automatically adjust and improve upon existing systems.

Predictive Analytics and DaaS Use Rules and Tracking to Make Forecasting and Automation Easier

Studies have shown that predictive analytics, which use rulesets to review data collected in a facility, can see up to 10 to 25 percent greater savings in HVAC systems alone for energy, with most users reporting an average decrease in overall facilities management costs, asserts Dr. Atul Sharma. But, how do predictive analytics actually save money?

Predictive analytics systems can assess the current and future state of specific equipment, such as HVAC systems, by reviewing its existing energy consumption rates, work performed, operating time and factors for trends and patterns. The system then populates this information through dashboarding tools and business intelligence, making it easier for facility managers to successfully mitigate risks that arise during operation.

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Business intelligence and dashboarding lead back to DaaS, in which companies provide the means of collecting, scrubbing and analyzing data. Since these systems are intrinsically similar, DaaS may be used in lieu of predictive analytics among facility managers. Moreover, the sub-technologies within predictive analytics, its algorithms and sensors collecting data, can identify potential issues and ways to improve an existing systems operational and energy efficiency without sacrificing comfort. Furthermore, predictive analytics leads directly into the use of machine learning.

This information may include demand and performance measurements, energy costs, tariffs or surcharges, occupancy rates, comfort rates, complaints about the environment within a facility, and system data. By reviewing the information for trends, correlations and variances, it allows automated systems to create advanced forecasts in both new and common scenarios. As a result, machine learning can be leveraged to run what if scenarios to reflect risks in facilities management and select the best solution to correct and optimize operations. Since the system does more than analyze data, it feeds back into the initial data capture, collection and analysis within DaaS and predictive analytics systems. This creates a self-fulfilling cycle that grows stronger as more data is generated.

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Leverage the Power of Information Capture and Processing Power in Energy Management Technology in Facilities Management Now

Today’s building engineers and Architects need to find ways to reduce facilities carbon emissions resulting from energy use. Meanwhile, the cost of material resources during construction and maintenance has increased, so the only way to ensure a building’s cost-effectiveness is by instilling advanced energy management technology that actively works to reduce overhead costs and improve the experience of building occupants, comprised of tenants, stakeholders, residents, employees or otherwise. To learn more about how energy management technology is changing the game when it comes to the successful management and operation of your facility, {{cta(‘fc00abb8-b5c5-489f-805a-347ff0eed124’)}} today.